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BMC Syst Biol. 2012; 6: 30.
Published online May 1, 2012. doi:  10.1186/1752-0509-6-30
PMCID: PMC3423039
Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions
Jeffrey D Orth1 and BernhardØ Palssoncorresponding author1
1Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412, USA
corresponding authorCorresponding author.
Jeffrey D Orth: jorth/at/ucsd.edu; BernhardØ Palsson: palsson/at/ucsd.edu
Received January 16, 2012; Accepted May 1, 2012.
Abstract
Background
The iJO1366 reconstruction of the metabolic network of Escherichia coli is one of the most complete and accurate metabolic reconstructions available for any organism. Still, because our knowledge of even well-studied model organisms such as this one is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the network.
Results
A new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions.
Conclusions
Numerous improvements to the iJO1366 metabolic reconstruction were suggested by these analyses. Experiments were performed to verify several computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking.
Keywords: Constraint-based modeling, Metabolic network reconstruction, Escherichia coli, Gap-filling, Gene annotation
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